We show that cellular automata can classify data by inducing a form of dynamical phase coexistence. We use Monte Carlo methods to search for general two-dimensional deterministic automata that classify images on the basis of activity, the number of state changes that occur in a trajectory initiated from the image. When the number of timesteps of the automaton is a trainable parameter, the search scheme identifies automata that generate a population of dynamical trajectories displaying high or low activity, depending on initial conditions. Automata of this nature behave as nonlinear activation functions with an output that is effectively binary, resembling an emergent version of a spiking neuron.

翻译：我们显示蜂窝自动数据可以通过诱导一种动态相位共存的形式对数据进行分类。 我们使用蒙特卡洛方法搜索基于活动对图像进行分类的一般二维确定性自动数据, 即从图像开始的轨迹中发生的状态变化的数量。 当自动映射的时步数是一个可训练的参数时, 搜索方案将自动映射生成显示高度或低活动活动的动态轨迹群的自动映射数据, 取决于初始条件。 这种性质的自动映射将表现为非线性激活功能, 其输出是有效的二进制, 与突现式神经元相交 。